Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while also examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.

翻译：边缘人工智能（Edge AI）构建了一个由相互连接的系统与设备组成的网络，这些系统与设备在数据采集位置附近，通过人工智能技术接收、缓存、处理和分析数据。近年来人工智能效率的提升、物联网（IoT）设备的广泛应用以及边缘计算的兴起，共同释放了边缘人工智能的巨大潜力。边缘人工智能的目标是在确保数据机密性与完整性的同时，优化数据处理效率与速度。尽管作为一个相对新兴的研究领域（时间跨度从2014年至今），它在过去五年中展现出显著而快速的发展。本文针对边缘人工智能进行了系统性文献综述，以探讨现有研究、近期进展及未来研究方向。我们构建了一个面向云与边缘计算分析的协同边缘人工智能学习系统，并对支持该机制的架构进行了深入研究。所提出的边缘人工智能分类体系有助于对边缘人工智能系统进行分类与配置，同时通过涵盖基础设施、云计算、雾计算、服务、应用场景、机器学习与深度学习以及资源管理等多个维度，审视其跨领域的潜在影响。本研究强调了边缘人工智能在网络边缘处理实时数据的重要性，并着重指出了边缘人工智能系统面临的研究挑战，包括资源约束、安全威胁脆弱性以及可扩展性问题。最后，本研究展望了未来可能的研究方向，这些方向旨在通过提供创新性解决方案来突破边缘人工智能当前的局限性。